Ensembled deep learning for the classification of human sperm head morphology

Infertility is a growing global health concern, with male factor infertility contributing to half of all cases. Semen analysis is crucial to infertility diagnostics. However, sperm morphology assessment, as a routine part of analysis, is still performed manually and is thus highly subjective. Here, a stacked ensemble of convolutional neural networks (CNNs) is presented for automated classification of human sperm head morphology. By combining traditional CNN models with modern residual and densely connected architectures using a multi-class meta-classifier, classification rate improvements of 2.7% (to 98.2%) and 2.3% (to 63.3%) on the HuSHeM and SCIAN-MorphoSpermGS (SCIAN) datasets, respectively, are achieved. This considerable improvement in prediction performance is achieved as the meta-classifier improves upon the individual classification rates of the base models by ≈8.5%. The ensembled deep learning model is a powerful step toward an automated sperm morphology analysis, providing new opportunities to standardize clinical practice and reduce treatment costs to improve patient treatment.